Big data analytics and knowledge discovery : 19th International Conference, DaWaK 2017, Lyon, France, August 28-31, 2017, Proceedings. DaWaK (Conference) (19th : 2017 : Lyon, France)最新文献
Pub Date : 2023-01-01DOI: 10.1007/978-3-031-39831-5
{"title":"Big Data Analytics and Knowledge Discovery: 25th International Conference, DaWaK 2023, Penang, Malaysia, August 28–30, 2023, Proceedings","authors":"","doi":"10.1007/978-3-031-39831-5","DOIUrl":"https://doi.org/10.1007/978-3-031-39831-5","url":null,"abstract":"","PeriodicalId":92483,"journal":{"name":"Big data analytics and knowledge discovery : 19th International Conference, DaWaK 2017, Lyon, France, August 28-31, 2017, Proceedings. DaWaK (Conference) (19th : 2017 : Lyon, France)","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87122415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1007/978-3-031-12670-3
{"title":"Big Data Analytics and Knowledge Discovery: 24th International Conference, DaWaK 2022, Vienna, Austria, August 22–24, 2022, Proceedings","authors":"","doi":"10.1007/978-3-031-12670-3","DOIUrl":"https://doi.org/10.1007/978-3-031-12670-3","url":null,"abstract":"","PeriodicalId":92483,"journal":{"name":"Big data analytics and knowledge discovery : 19th International Conference, DaWaK 2017, Lyon, France, August 28-31, 2017, Proceedings. DaWaK (Conference) (19th : 2017 : Lyon, France)","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90542164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1007/978-3-030-86534-4
{"title":"Big Data Analytics and Knowledge Discovery: 23rd International Conference, DaWaK 2021, Virtual Event, September 27–30, 2021, Proceedings","authors":"","doi":"10.1007/978-3-030-86534-4","DOIUrl":"https://doi.org/10.1007/978-3-030-86534-4","url":null,"abstract":"","PeriodicalId":92483,"journal":{"name":"Big data analytics and knowledge discovery : 19th International Conference, DaWaK 2017, Lyon, France, August 28-31, 2017, Proceedings. DaWaK (Conference) (19th : 2017 : Lyon, France)","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82077215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-01-01DOI: 10.1007/978-3-030-59065-9
Min Song, I. Song, G. Kotsis, A. Tjoa, Ismail Khalil
{"title":"Big Data Analytics and Knowledge Discovery: 22nd International Conference, DaWaK 2020, Bratislava, Slovakia, September 14–17, 2020, Proceedings","authors":"Min Song, I. Song, G. Kotsis, A. Tjoa, Ismail Khalil","doi":"10.1007/978-3-030-59065-9","DOIUrl":"https://doi.org/10.1007/978-3-030-59065-9","url":null,"abstract":"","PeriodicalId":92483,"journal":{"name":"Big data analytics and knowledge discovery : 19th International Conference, DaWaK 2017, Lyon, France, August 28-31, 2017, Proceedings. DaWaK (Conference) (19th : 2017 : Lyon, France)","volume":"75 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80605028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-01-01DOI: 10.1007/978-3-030-27520-4
C. Ordonez, I. Song, Gabriele Anderst-Kotsis, A. Tjoa, Ismail Khalil
{"title":"Big Data Analytics and Knowledge Discovery: 21st International Conference, DaWaK 2019, Linz, Austria, August 26–29, 2019, Proceedings","authors":"C. Ordonez, I. Song, Gabriele Anderst-Kotsis, A. Tjoa, Ismail Khalil","doi":"10.1007/978-3-030-27520-4","DOIUrl":"https://doi.org/10.1007/978-3-030-27520-4","url":null,"abstract":"","PeriodicalId":92483,"journal":{"name":"Big data analytics and knowledge discovery : 19th International Conference, DaWaK 2017, Lyon, France, August 28-31, 2017, Proceedings. DaWaK (Conference) (19th : 2017 : Lyon, France)","volume":"221 3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83678873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-08-01Epub Date: 2017-08-03DOI: 10.1007/978-3-319-64283-3_11
Rundong Li, Ningfang Mi, Mirek Riedewald, Yizhou Sun, Yi Yao
We consider data analytics workloads on distributed architectures, in particular clusters of commodity machines. To find a job partitioning that minimizes running time, a cost model, which we more accurately refer to as makespan model, is needed. In attempting to find the simplest possible, but sufficiently accurate, such model, we explore piecewise linear functions of input, output, and computational complexity. They are abstract in the sense that they capture fundamental algorithm properties, but do not require explicit modeling of system and implementation details such as the number of disk accesses. We show how the simplified functional structure can be exploited by directly integrating the model into the makespan optimization process, reducing complexity by orders of magnitude. Experimental results provide evidence of good prediction quality and successful makespan optimization across a variety of cluster architectures.
{"title":"A Case for Abstract Cost Models for Distributed Execution of Analytics Operators.","authors":"Rundong Li, Ningfang Mi, Mirek Riedewald, Yizhou Sun, Yi Yao","doi":"10.1007/978-3-319-64283-3_11","DOIUrl":"https://doi.org/10.1007/978-3-319-64283-3_11","url":null,"abstract":"<p><p>We consider data analytics workloads on distributed architectures, in particular clusters of commodity machines. To find a job partitioning that minimizes running time, a cost model, which we more accurately refer to as makespan model, is needed. In attempting to find the simplest possible, but sufficiently accurate, such model, we explore piecewise linear functions of input, output, and computational complexity. They are abstract in the sense that they capture fundamental algorithm properties, but do not require explicit modeling of system and implementation details such as the number of disk accesses. We show how the simplified functional structure can be exploited by directly integrating the model into the makespan optimization process, reducing complexity by orders of magnitude. Experimental results provide evidence of good prediction quality and successful makespan optimization across a variety of cluster architectures.</p>","PeriodicalId":92483,"journal":{"name":"Big data analytics and knowledge discovery : 19th International Conference, DaWaK 2017, Lyon, France, August 28-31, 2017, Proceedings. DaWaK (Conference) (19th : 2017 : Lyon, France)","volume":"10440 ","pages":"149-163"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/978-3-319-64283-3_11","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"36824483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Big data analytics and knowledge discovery : 19th International Conference, DaWaK 2017, Lyon, France, August 28-31, 2017, Proceedings. DaWaK (Conference) (19th : 2017 : Lyon, France)
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